Scholarlink

<p>Machine learning is revolutionizing various fields, but its implementation in real-time soft environments often faces challenges due to limited computational and storage resources. In this work, we have successfully developed a highly accurate Random Forest regression model to predict the working ambient temperature for an embedded Radio Access Network system, particularly within the Baseband application domain. Our model achieves minimal prediction error and maintains a variance well aligned with the onboard sensors’ measurement accuracy. Remarkably, the outcomes of our research respect the stringent real-time processing and storage constraints, making it a significant advancement in real-time machine learning applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning-based ambient temperature prediction in radio access network environments

  • 期刊论文

  • 发表日期：

    2025年2月1日

查看全文
  • Selma Rahman,
  • Mattias Olausson,
  • Carlo Vitucci,
  • Ioannis Avgouleas

摘要

Machine learning is revolutionizing various fields, but its implementation in real-time soft environments often faces challenges due to limited computational and storage resources. In this work, we have successfully developed a highly accurate Random Forest regression model to predict the working ambient temperature for an embedded Radio Access Network system, particularly within the Baseband application domain. Our model achieves minimal prediction error and maintains a variance well aligned with the onboard sensors’ measurement accuracy. Remarkably, the outcomes of our research respect the stringent real-time processing and storage constraints, making it a significant advancement in real-time machine learning applications.